out, _ := exec.Command("/bin/ls").CombinedOutput() // Compliantout, _ := exec.Command("/bin/ls").CombinedOutput()out, _ := exec.Command("/bin/ls").CombinedOutput() // Compliantout, _ := exec.Command("/bin/ls").CombinedOutput()This rule raises an issue when a NumPy function expecting a square matrix is called with a non-square matrix.
+Some matrix operations only make sense on square matrices. For example, a determinant can be computed only if a matrix is square.
+Therefore, NumPy functions performing these operations should be provided with a square matrix as an argument. If something other than a square matrix is provided, the computation does not make sense and an exception will be raised.
+The following functions will raise an exception when provided with a non-square matrix:
+np.linalg.det(a): computes the determinant of an matrix.
np.linalg.eig(a): computes the eigenvalues and right eigenvectors of a square matrix.
np.linalg.matrix_power(a, n): raises a square matrix to the (integer) power n.
np.linalg.solve(a, b): Solve a linear matrix equation, or system of linear scalar equations. a should be square.
np.linalg.inv(a): computes the (multiplicative) inverse of a matrix.
Also requires a non-singular matrix (a matrix is singular if its determinant is 0).
+You can use numpy.shape() function to check the dimensions of the matrix before performing the operation.
import numpy as np
+
+a = np.array([[1, 2], [3, 4], [5, 6]])
+b = np.linalg.inv(a) # Noncompliant: LinAlgError: Last 2 dimensions of the array must be squareimport numpy as np
+
+a = np.array([[1, 2], [3, 4]])
+b = np.linalg.inv(a) # CompliantNumPy Documentation - numpy.linalg.det
+NumPy Documentation - numpy.linalg.eig
+NumPy Documentation - numpy.linalg.inv
+NumPy Documentation - numpy.linalg.matrix_power
+NumPy Documentation - numpy.linalg.solve
+NumPy Documentation - numpy.shape
+(visible only on this page)
+(visible only on this page)
+This rule raises an issue when a NumPy function expecting a square matrix is called with a non-square matrix.
+Some matrix operations only make sense on square matrices. For example, a determinant can be computed only if a matrix is square.
+Therefore, NumPy functions performing these operations should be provided with a square matrix as an argument. If something other than a square matrix is provided, the computation does not make sense and an exception will be raised.
+The following functions will raise an exception when provided with a non-square matrix:
+np.linalg.det(a): computes the determinant of an matrix.
np.linalg.eig(a): computes the eigenvalues and right eigenvectors of a square matrix.
np.linalg.matrix_power(a, n): raises a square matrix to the (integer) power n.
np.linalg.solve(a, b): Solve a linear matrix equation, or system of linear scalar equations. a should be square.
np.linalg.inv(a): computes the (multiplicative) inverse of a matrix.
Also requires a non-singular matrix (a matrix is singular if its determinant is 0).
+You can use numpy.shape() function to check the dimensions of the matrix before performing the operation.
import numpy as np
+
+a = np.array([[1, 2], [3, 4], [5, 6]])
+b = np.linalg.inv(a) # Noncompliant: LinAlgError: Last 2 dimensions of the array must be squareimport numpy as np
+
+a = np.array([[1, 2], [3, 4]])
+b = np.linalg.inv(a) # CompliantNumPy Documentation - numpy.linalg.det
+NumPy Documentation - numpy.linalg.eig
+NumPy Documentation - numpy.linalg.inv
+NumPy Documentation - numpy.linalg.matrix_power
+NumPy Documentation - numpy.linalg.solve
+NumPy Documentation - numpy.shape
+(visible only on this page)
+(visible only on this page)
+